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\chapter*{ABSTRACT}


Reliability is a critical metric for organizations since it directly
influences their performance in face of the market competition, as
well as is essential in maintaining their production systems
available. The prediction of such quantitaive metric is then of great
interest, as it may anticipate the knowledge about system failures and
let organizations avoid and/or overcome such undesirable situations.
Systems' reliability depends on the inherent aging factors as well as
on the operational conditions the system is subjected to. This may
render the reliability modelling very complex and then traditional
stochastic processes fail to accurately predict its behavior in
time. In this context, learning methods such as Support Vector
Machines (SVMs) emerge as alternative to tackle these
shortcomings. One of the main advantages of using SVMs is the fact
that they do not require previous knowledge about the function or
process that maps input variables into output. However, their
performances are affected by a set of parameters that appear in the
related learning problems. This gives rise to the SVM model selection
problem, which consists in choosing the most suitable values for these
parameters. In this work, this problem is solved by means of Particle
Swarm Optimization, a probabilistic approach based on the behavior of
biological organisms that move in groups. Moreover, a PSO+SVM
methodology is proposed to handle reliability prediction problems,
which is validated by the resolution of examples from literature based
on time series data. The obtained results, compared to the ones
provided by other prediction tools such as Neural Networks (NNs),
indicate that the proposed methodology is able to provide competitive
or even more accurate reliability predictions. Also, the proposed
PSO+SVM is applied to an example application involving data collected
from oil production wells.

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\noindent{\bf Keywords:} Support Vector Machines, Particle Swarm Optimization,
Reliability Prediction.

